{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-04-24T07:50:48.042745Z",
     "start_time": "2025-04-24T07:50:47.774740Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-24T07:51:08.895284Z",
     "start_time": "2025-04-24T07:50:48.123210Z"
    }
   },
   "cell_type": "code",
   "source": [
    "mnist = fetch_openml('mnist_784', version=1)\n",
    "X, y = mnist.data, mnist.target.astype(np.int8)"
   ],
   "id": "7d3d102771c6b105",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\PythonProject\\Anaconda3-2024\\envs\\test\\lib\\site-packages\\sklearn\\datasets\\_openml.py:1022: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
      "  warn(\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-24T07:51:09.604859Z",
     "start_time": "2025-04-24T07:51:08.900338Z"
    }
   },
   "cell_type": "code",
   "source": [
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ],
   "id": "97d22a9f4be91203",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-24T07:51:10.630990Z",
     "start_time": "2025-04-24T07:51:09.889595Z"
    }
   },
   "cell_type": "code",
   "source": "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
   "id": "4f2d79c74624182f",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-24T07:52:12.639293Z",
     "start_time": "2025-04-24T07:51:10.678546Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, classification_report\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "# Logistic回归模型训练\n",
    "model = LogisticRegression(max_iter=1000)\n",
    "model.fit(X_train, y_train)"
   ],
   "id": "5871f2b341d8d145",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ],
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      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
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     "end_time": "2025-04-24T07:52:12.810564Z",
     "start_time": "2025-04-24T07:52:12.779542Z"
    }
   },
   "cell_type": "code",
   "source": "y_pred = model.predict(X_test)\n",
   "id": "888f2e49e7ef4c90",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-24T07:52:12.858284Z",
     "start_time": "2025-04-24T07:52:12.815282Z"
    }
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   "cell_type": "code",
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision, recall, f1, _ = precision_recall_fscore_support(y_test, y_pred, average='weighted')\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "class_report = classification_report(y_test, y_pred)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1-score:\", f1)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "print(\"Classification Report:\\n\", class_report)"
   ],
   "id": "1fb1b645a8e18dee",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9165714285714286\n",
      "Precision: 0.9163392934584369\n",
      "Recall: 0.9165714285714286\n",
      "F1-score: 0.9163608775081777\n",
      "Confusion Matrix:\n",
      " [[1286    1   11    0    1   11   20    4    6    3]\n",
      " [   0 1556    7   10    3    6    1    4   11    2]\n",
      " [   6   19 1234   18   13   13   20   17   29   11]\n",
      " [   7    9   37 1272    1   39    7   22   19   20]\n",
      " [   5    2   11    4 1189    5   12    9    9   49]\n",
      " [  12    9   10   42   12 1114   20    2   36   16]\n",
      " [   7    5   17    1   17   21 1320    3    5    0]\n",
      " [   4    4   22    5   13    6    0 1415    2   32]\n",
      " [  11   31   16   41    5   44   13    9 1166   21]\n",
      " [   6    9    6   15   36    5    0   51   12 1280]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.96      0.96      1343\n",
      "           1       0.95      0.97      0.96      1600\n",
      "           2       0.90      0.89      0.90      1380\n",
      "           3       0.90      0.89      0.90      1433\n",
      "           4       0.92      0.92      0.92      1295\n",
      "           5       0.88      0.88      0.88      1273\n",
      "           6       0.93      0.95      0.94      1396\n",
      "           7       0.92      0.94      0.93      1503\n",
      "           8       0.90      0.86      0.88      1357\n",
      "           9       0.89      0.90      0.90      1420\n",
      "\n",
      "    accuracy                           0.92     14000\n",
      "   macro avg       0.92      0.92      0.92     14000\n",
      "weighted avg       0.92      0.92      0.92     14000\n",
      "\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-24T07:52:13.268018Z",
     "start_time": "2025-04-24T07:52:12.922559Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title(\"Logistic Regression - Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ],
   "id": "c76d0eb4bf2c4cbf",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "jupyter": {
     "is_executing": true
    },
    "ExecuteTime": {
     "start_time": "2025-04-24T07:52:13.317988Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, classification_report, ConfusionMatrixDisplay\n",
    "\n",
    "# 1. 加载 MNIST 数据集\n",
    "mnist = fetch_openml('mnist_784', version=1, as_frame=False, parser='auto')\n",
    "X, y = mnist.data, mnist.target.astype(np.uint8)  # (70000, 784), 目标值转整数\n",
    "\n",
    "# 2. 数据预处理（归一化 & 训练集划分）\n",
    "X = X.astype(np.float32) / 255.0  # 先转换为 float32，再归一化  # 归一化到 [0,1]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "\n",
    "# 3. 训练 SVM 模型（RBF 核）\n",
    "svm_model = SVC(kernel='rbf', C=10, gamma=0.01)  # 选择适合的 C 和 gamma\n",
    "# from sklearn.svm import LinearSVC\n",
    "# svm_model = LinearSVC(C=1.0, max_iter=1000, dual=False)  # dual=False 适用于大数据\n",
    "svm_model.fit(X_train, y_train)\n",
    "\n",
    "# 4. 预测\n",
    "y_pred = svm_model.predict(X_test)\n",
    "\n",
    "# 5. 计算准确率\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"测试集准确率: {accuracy:.4f}\")\n",
    "\n",
    "# 6. 分类报告\n",
    "print(\"\\n分类报告:\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "# 7. 可视化部分预测错误的图片\n",
    "misclassified = np.where(y_pred != y_test)[0]  # 获取错误分类的索引\n",
    "fig, axes = plt.subplots(2, 5, figsize=(10, 5))\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    idx = misclassified[i]\n",
    "    ax.imshow(X_test[idx].reshape(28, 28), cmap='gray')\n",
    "    ax.set_title(f\"Pred: {y_pred[idx]}\\nTrue: {y_test[idx]}\")\n",
    "    ax.axis(\"off\")\n",
    "plt.show()\n",
    "\n",
    "# 8. 绘制混淆矩阵\n",
    "ConfusionMatrixDisplay.from_estimator(svm_model, X_test, y_test, cmap=\"Blues\")\n",
    "plt.show()"
   ],
   "id": "d3eb2e6a787d451a",
   "outputs": [],
   "execution_count": null
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
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